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A semiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model

Author

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  • Reisen, Valdério A.
  • Zamprogno, Bartolomeu
  • Palma, Wilfredo
  • Arteche, Josu
Abstract
This paper explores seasonal and long-memory time series properties by using the fractional ARIMA model when the data have one and two seasonal periods and short-memory components. The stationarity and invertibility parameter conditions are established for the model studied. To estimate the seasonal fractional long-memory parameters, a semiparametric estimation method is proposed. The asymptotic properties of the estimator are established and the accuracy of the method is investigated through Monte Carlo experiments. The good performance of the estimator indicates that it can be an alternative procedure to estimate long-memory time series data with two seasonal periods. Series of PM10 concentrations and electricity hourly demand are considered as examples of applications of the proposed estimation method.

Suggested Citation

  • Reisen, Valdério A. & Zamprogno, Bartolomeu & Palma, Wilfredo & Arteche, Josu, 2014. "A semiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 1-17.
    • Handle: RePEc:eee:matcom:v:98:y:2014:i:c:p:1-17
    DOI: 10.1016/j.matcom.2013.11.001
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    1. L. A. Gil‐Alana, 2001. "Testing Stochastic Cycles in Macroeconomic Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(4), pages 411-430, July.
    2. Wilfredo Palma & Ngai Hang Chan, 2005. "Efficient Estimation of Seasonal Long‐Range‐Dependent Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(6), pages 863-892, November.
    3. Clifford M. Hurvich & Rohit Deo & Julia Brodsky, 1998. "The mean squared error of Geweke and Porter‐Hudak's estimator of the memory parameter of a long‐memory time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 19-46, January.
    4. J. Arteche & C. Velasco, 2005. "Trimming and Tapering Semi‐Parametric Estimates in Asymmetric Long Memory Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 581-611, July.
    5. Josu Arteche & Peter M. Robinson, 2000. "Semiparametric Inference in Seasonal and Cyclical Long Memory Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(1), pages 1-25, January.
    6. Franco, Glaura C. & Reisen, Valderio A., 2007. "Bootstrap approaches and confidence intervals for stationary and non-stationary long-range dependence processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 546-562.
    7. Henry L. Gray & Nien‐Fan Zhang & Wayne A. Woodward, 1989. "On Generalized Fractional Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 233-257, May.
    8. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.
    9. Sun, Yixiao & Phillips, Peter C. B., 2003. "Nonlinear log-periodogram regression for perturbed fractional processes," Journal of Econometrics, Elsevier, vol. 115(2), pages 355-389, August.
    10. Man, K. S., 2003. "Long memory time series and short term forecasts," International Journal of Forecasting, Elsevier, vol. 19(3), pages 477-491.
    11. Remigijus Leipus & Marie‐Claude Viano, 2000. "Modelling Long‐memory Time Series with Finite or Infinite Variance: a General Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(1), pages 61-74, January.
    12. Valdério A. Reisen & Eric Moulines & Philippe Soulier & Glaura C. Franco, 2010. "On the properties of the periodogram of a stationary long‐memory process over different epochs with applications," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(1), pages 20-36, January.
    13. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    14. Uwe Hassler, 1993. "Regression Of Spectral Estimators With Fractionally Integrated Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(4), pages 369-380, July.
    15. Josu Arteche, 2002. "Semiparametric robust tests on seasonal or cyclical long memory time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(3), pages 251-285, May.
    16. Marques, G.O.L.C., 2011. "Empirical aspects of the Whittle-based maximum likelihood method in jointly estimating seasonal and non-seasonal fractional integration parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 8-17.
    17. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    18. Reisen, Valderio Anselmo & Rodrigues, Alexandre L. & Palma, Wilfredo, 2006. "Estimation of seasonal fractionally integrated processes," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 568-582, January.
    19. Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
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    3. Taghreed Alghamdi & Sifatul Mostafi & Ghadeer Abdelkader & Khalid Elgazzar, 2022. "A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior," Future Internet, MDPI, vol. 14(10), pages 1-21, October.
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